Tropical and Sub-Tropical Flood and Water Masks

10 m spatial resolution flood and water masks that can be used to develop machine learning models to classify flooding in satellite images.

The flood and water masks are generated by combining various datasets including the ESA WorldCover 10m v100 and vector flood and water layers from the Copernicus Emergency Management System (EMS) Rapid Mapping Activation events.

In total, 513 flood and water mask layers were generated as GeoTIFF files with a 10 m spatial resolution. Each of these layers corresponds to a flood event and date (there can be many flood mask layers per-event which represent flooding on different days as the event evolves). Flood and water masks were generated for 65 flood events since 2018 in 26 countries spanning the tropics and sub-tropics. This broad scale dataset can be used to train flood detection models, that can detect and segment flooding in satellite images, and be fine-tuned for Pacific geographic contexts.

Each flood and water mask is a raster image dataset where pixel values correspond to the following classes:

• 1: land and not flooded
• 2: flooding (rasterised from the observed events layers in the EMS Rapid Mapping Activation data)
• 3: permanent water (determined by the water class in the ESA WorldCover 10m v100 product or hydrography layers in the EMS Rapid Mapping Activation data)

Each flood and water mask has three dates associated with it (these can be found in metadata.csv):

• activation date: the date of the activation in the EMS Rapid Mapping Activation system.
• event date: the initial date of the flood event (this can be before the activation date).
• satellite date: the date of the latest input images and data used to generate the observed event data in EMS Rapid Mapping Activation.

Dataset generation

The source code used to generate the dataset is published on GitHub.

Acknowledgements

This dataset was generated through a project funded by the Climate Change AI Innovation Grants Program. We would also like to acknowledge the ml4floods package and Google Earth Engine which provided some functions used to generate this dataset and the Copernicus Emergency Management Service and European Space Agency (ESA; WorldCover 10 m 2020) for providing access to the underlying data.